DataLLM LabDeepSeek V4-Flash Review: 9/9 at 1/31 the Cost of Opus
DeepSeek V4-Flash is the efficiency tier of DeepSeek's V4 family - and in our own executed coding test it did something its price tag says it shouldn't: it matched Claude Opus 4.8's perfect 9-of-9 score, at roughly one thirty-first of the cost, and it even out-scored its larger sibling V4-Pro. This review covers what V4-Flash is, the tested results (with the honest caveats), the real pricing, and exactly when to reach for it.
What DeepSeek V4-Flash is
DeepSeek V4-Flash is the efficiency tier of DeepSeek's V4 family, released April 24, 2026. It's a Mixture-of-Experts model — roughly 284B total parameters with ~13B activated per token — with a native 1M-token context and open weights. The headline is price: it's one of the cheapest frontier-tier models you can call, and as we found, that cheapness doesn't cost you correctness on routine coding.
What we tested
We ran nine coding tasks — chosen to separate models, not flatter them — across 13 models, executing each answer against hidden tests. The tasks: two-sum, valid-parentheses, merge-intervals, Roman-to-integer, longest-common-subsequence, a nested-dict flatten, top-k words, a token-bucket limiter, and a CSV-line parser (quoted fields with escaped quotes). Same set as our coding-cost benchmark.
Results: 9/9, for cents
V4-Flash solved all nine — the same perfect score as Claude Opus 4.8 — at a tiny fraction of the cost. Here's the full tested field, by real billed cost per 1,000 tasks:
| Model | Score | Missed | $/1,000 tasks | Avg latency |
|---|---|---|---|---|
| Claude Opus 4.8 | 9/9 | — | $4.05 | 6.1s |
| GLM 5.2 | 9/9 | — | $1.99 | 12.3s |
| Kimi K2.7-Code | 9/9 | — | $1.34 | 10.4s |
| MiniMax M3 | 9/9 | — | $0.90 | 13.4s |
| DeepSeek V4-Pro | 8/9 | CSV parse | $0.74 | 18.2s |
| DeepSeek V4-Flash | 9/9 | — | $0.13 | 14.5s |
| Qwen3 Coder Next | 9/9 | — | $0.10 | 7.0s |
The takeaway is blunt: V4-Flash matched the most expensive model's correctness at ~1/31st of its cost, and Qwen3 Coder Next was a hair cheaper still. On these tasks, paying frontier rates bought nothing extra.
The V4-Flash beats V4-Pro surprise
The counterintuitive result: the small Flash out-scored the big Pro. V4-Pro (the 1.6T-parameter flagship) managed 8/9 — it tripped on the CSV-line parser — while V4-Flash went 9/9, at roughly 1/6th of Pro's cost. Bigger isn't automatically better on bounded coding tasks. (More on that decision in our V4-Pro vs V4-Flash guide.)
The catch: speed
V4-Flash is cheap and accurate — not fast. It generates reasoning before answering (it spent ~568 reasoning tokens per task), so it averaged ~14.5 seconds per task, versus Opus 4.8's ~6.1s and Qwen3 Coder Next's ~7.0s. So the honest positioning is: if cost-per-correct-answer is what you optimize, V4-Flash wins; if you need snappy interactive latency, a non-thinking coder like Qwen may fit better.
Pricing & access
On OpenRouter (deepseek/deepseek-v4-flash): about $0.09 input / $0.18 output per 1M tokens — the rate our test billed at. DeepSeek's first-party cache-miss rate is quoted slightly higher (around $0.14 / $0.28), with cache hits dropping to roughly $0.0028/1M (a ~98% discount on repeated context). The weights are open, so self-hosting is possible, though the 284B MoE needs real hardware. Note: DeepSeek's legacy deepseek-chat / deepseek-reasoner aliases are being deprecated on 2026-07-24 in favor of the V4 ids.
from openai import OpenAI
client = OpenAI(base_url="https://openrouter.ai/api/v1", api_key="YOUR_KEY")
resp = client.chat.completions.create(
model="deepseek/deepseek-v4-flash",
messages=[{"role": "user", "content": "Refactor this function..."}],
)Who should use it
High-volume coding
- Frontier-level correctness at cents per thousand tasks — the best cost-per-correct-answer we measured.
Cheap-first routing
- Default tier for an agent, escalating only hard tasks to Opus or V4-Pro.
Long context on a budget
- Native 1M context with cache discounts for big-repo work.
Not for latency-critical UX
- It reasons first; for sub-second feel, pick a non-thinking coder.
Route DeepSeek V4-Flash and 300+ models with one key
Default to V4-Flash for the cheap, correct bulk; escalate to Claude Opus on the hard tasks — one OpenAI-compatible endpoint, with failover.
FAQ
What is DeepSeek V4-Flash?
The efficiency tier of DeepSeek's V4 family (Apr 24, 2026) — ~284B total / ~13B active MoE, native 1M context, open weights. One of the cheapest frontier-tier models (~$0.09/$0.18 on OpenRouter).
Is DeepSeek V4-Flash good for coding?
Yes — 9/9 in our executed test, matching Claude Opus 4.8, at ~1/31st the cost. The trade-off is latency (~15s/task; it reasons before answering).
How much does DeepSeek V4-Flash cost?
~$0.09/$0.18 per 1M on OpenRouter (first-party ~$0.14/$0.28 cache-miss; cache hits ~$0.0028/1M). About $0.13 per 1,000 tasks in our test, vs $4.05 for Opus 4.8.
V4-Flash vs V4-Pro — which?
Flash for routine coding: 9/9 vs Pro's 8/9 at ~1/6th the cost in our test. Reach for the bigger Pro only on genuinely harder problems.
Is DeepSeek V4-Flash open source?
Yes — open-weights under a permissive license; self-hostable, though the 284B MoE needs serious hardware, so most use a hosted API.
Is it faster than other models?
No — cheap and accurate, not fast (~15s/task because it reasons). Non-thinking coders like Qwen3 Coder Next (~2.8s) are faster.
Can I use it with one key alongside other models?
Yes — via an OpenAI-compatible gateway like DataLLM Lab, call deepseek/deepseek-v4-flash and 300+ others (incl. Opus for escalation) with one key.
What are the test's limits?
Nine standard tasks, one run, pass/fail via executed code — a cost-efficiency check, not agentic/repo-scale. SWE-bench Verified 79.0 is vendor-reported.